Closed fyting closed 1 year ago
Fig 2 was drawn using excel and ppt. I first used excel to draw a line graph and then used ppt to add texts. The table in the lower right corner was added later in latex.
Thank you for your response. “The table in the lower right corner was added later in latex.”, can you share the latex code?
\begin{wrapfigure}{r}{0.5\textwidth}
\includegraphics[width=0.99\linewidth]{figure/param_ap.pdf}
\vspace{-39.0mm}\hspace{30.2mm}
\resizebox{0.26\columnwidth}{!}{\tablestyle{2pt}{1}
\renewcommand\arraystretch{0.55}
\input{table/fig2.tex}
}
\vspace{9mm}
\caption{
\textbf{Object detection performance on COCO val2017 using Mask R-CNN.}
We see that the proposed ViT-Adapter brings significant improvements to plain ViTs.
$^\bigstar$~indecates using multi-modal pre-trained ViT from~\citep{zhu2021uni}.
Backbones pre-trained on ImageNet-22K are marked with $^\dagger$, otherwise ImageNet-1K.
}
\vspace{-3mm}
\label{fig:param_ap}
\end{wrapfigure}
\begin{tabular}[b]{l|c|c}
% \whline
\renewcommand{\arraystretch}{0.1}
Method & \#Param & AP$\rm ^b$ \\
\hline
PVTv2-B1 & 33.7M & 44.9 \\
ViT-T & 26.1M & 40.2 \\
\rowcolor{gray!20}
ViT-Adapter-T (ours) & 28.1M & 46.0 \\
\hline
PVTv2-B2 & 45.0M & 47.8 \\
Swin-T & 47.8M & 46.0 \\
ViT-S & 43.8M & 44.0 \\
\rowcolor{gray!20}
ViT-Adapter-S (ours) & 47.8M & 48.2 \\
\hline
Swin-B & 107.1M & 48.6 \\
ViT-B & 113.6M & 45.8 \\
\rowcolor{gray!20}
ViT-Adapter-B (ours) & 120.2M & 49.6 \\
\rowcolor{yellow!15}
ViT-Adapter-B$^\bigstar$(ours) & 120.2M & 51.2 \\
\hline
ViT-L$^\dagger$ & 337.3M & 48.8 \\
\rowcolor{gray!20}
ViT-Adapter-L$^\dagger$ (ours) & 347.9M & 52.1 \\
\end{tabular}
Thank you very much!
Hi, thx for your great work. Can you share the method to draw the figure 2 in your paper?